Camera calibration: adjusting camera lense edge distortion¶
by Uki D. Lucas
Motivation¶
The raw camera data usually has a certain level of distortion caused by lense shape, this is especially pronounced on the edges of the image. The correction is essential in applications like image recognition used in autonomous vehicles, robotics and even in 3D printing.
Solution approach¶
The common solution is to compare a known shape object e.g. a chessboard with the image taken, then calculate this specific camera's adjustment parameters that then can be applied to every frame taken by the camera. If the camera changes, the parameters have to be recalibrated.
Get list of sample chessboard images by this camera¶
In [1]:
# read a list of files using a parern
import glob
image_file_names = glob.glob("camera_cal/calibration*.jpg") # e.g. calibration19.jpg
print("found", len(image_file_names), "image file names" )
print("example", image_file_names[0])
In [2]:
#!/usr/bin/python3
import numpy as np
import cv2
import json
%matplotlib inline
Useful helper method¶
In [3]:
def plot_images(left_image, right_image):
import matplotlib.pyplot as plt
plt.figure(figsize=(20,10))
plot_image = np.concatenate((left_image, right_image), axis=1)
plt.imshow(plot_image)
plt.show()
In [4]:
def get_sample_gray(image_file_name: str):
image_original = cv2.imread(image_file_name)
# convert BGR image to gray-scale
image_gray = cv2.cvtColor(image_original, cv2.COLOR_BGR2GRAY)
return image_original, image_gray
The pattern will look for "inner corners" e.g. black touching the black square¶
In [5]:
# Chessboard dimentsions
# nx = 9 # horizontal
# ny = 6 # vertical
def find_inside_corners(image_file_names: list, nx: int=9, ny: int=6):
# Initialise arrays
# Object Points: 3d point in real world space
object_point_list = []
#Image Points: 2d points in image plane.
image_points_list = []
# Generate 3D object points
object_points = np.zeros((nx*ny, 3), np.float32)
object_points[:,:2] = np.mgrid[0:nx, 0:ny].T.reshape(-1, 2)
#print("first 5 elements:\n", object_points[0:5])
# see: http://docs.opencv.org/trunk/dc/dbb/tutorial_py_calibration.html
termination_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
chessboard_dimentions = (nx, ny)
import matplotlib.pyplot as plt
for image_file_name in image_file_names:
print("processing image:", image_file_name)
image_original, image_gray = get_sample_gray(image_file_name)
# Find the chess board corners
# Paramters:
# - image_gray
# - the chessboard to be used is 9x6
# - flags = None
has_found, corners = cv2.findChessboardCorners(image_gray, chessboard_dimentions, None)
if has_found == True:
# fill in ObjectPoints
object_point_list.append(object_points)
corners2 = cv2.cornerSubPix(image_gray, corners, (11,11), (-1,-1), termination_criteria)
# fill in ImagePoints
image_points_list.append(corners2)
# Draw and display the corners
# I have to clone/copy the image because cv2.drawChessboardCorners changes the content
image_corners = cv2.drawChessboardCorners(
image_original.copy(),
chessboard_dimentions,
corners2,
has_found)
plot_images(image_original, image_corners)
else: # not has_found
print("The", chessboard_dimentions, "chessboard pattern was not found")
plt.figure()
plt.imshow(image_original)
plt.show()
# end if
# end for
return object_point_list, image_points_list
In [6]:
object_point_list, image_points_list = find_inside_corners(image_file_names)
Calibrate using points¶
In [7]:
image_original, image_gray = get_sample_gray(image_file_names[1])
# Returns:
# - camera matrix
# - distortion coefficients
# - rotation vectors
# - translation vectors
has_sucess, matrix, distortion, rvecs, tvecs = cv2.calibrateCamera(
object_point_list,
image_points_list,
image_gray.shape[::-1],
None,
None)
This instead of concatinating edges, stretch/curve them¶
In [8]:
image_dimentions = image_original.shape[:2] # height, width
optimized_matrix, roi = cv2.getOptimalNewCameraMatrix(
matrix,
distortion,
image_dimentions,
1,
image_dimentions)
Remove the distortion form the images¶
In [9]:
for image_file_name in image_file_names:
print("removing distortion in", image_file_name)
image_original = cv2.imread(image_file_name)
print("without edge distortion (cropping)")
image1 = cv2.undistort(image_original, matrix, distortion, None, None)
plot_images(image_original, image1)
# save to disk
cv2.imwrite("output_images/" + "unoptimized_" + image_file_name, image1)
print("with edge distortion (curved)")
image2 = cv2.undistort(image_original, matrix, distortion, None, optimized_matrix )
plot_images(image_original, image2)
# save to disk
cv2.imwrite("output_images/" + "optimized_" + image_file_name, image2)
Conclusion¶
Assuming that I mask the curved edges, the optimized image provides a better camera calibration because it maintains more area of the image instead of cropping it. This is especially visible in the image camera_cal/calibration2.jpg
In [ ]: